Updated Global and Oceanic Mercury Budgets for the United Nations Global Mercury Assessment 2018
Bibliographic record
Abstract
In support of international efforts to reduce mercury (Hg) exposure in humans and wildlife, this paper reviews the literature concerning global Hg emissions, cycling and fate, and presents revised global and oceanic Hg budgets for the 2018 United Nations Global Mercury Assessment. We assessed two competing scenarios about the impacts of 16th - late 19th century New World silver (Ag) mining, which may be the largest human source of atmospheric Hg in history. Consideration of Ag ore geochemistry, historical documents on Hg use, and comparison of the scenarios against atmospheric Hg patterns in environmental archives, strongly support a "low mining emission" scenario. Building upon this scenario and other published work, the revised global budget estimates human activities including recycled legacy emissions have increased current atmospheric Hg concentrations by about 450% above natural levels (prevailing before 1450 AD). Current anthropogenic emissions to air are 2.5 ± 0.5 kt/y. The increase in atmospheric Hg concentrations has driven a ∼ 300% average increase in deposition, and a 230% increase in surface marine waters. Deeper marine waters show increases of only 12-25%. The overall increase in Hg in surface organic soils (∼15%) is small due to the large mass of natural Hg already present from rock weathering, but this figure varies regionally. Specific research recommendations are made to reduce uncertainties, particularly through improved understanding of fundamental processes of the Hg cycle, and continued improvements in emissions inventories from large natural and anthropogenic sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".